CamelliA-based simultaneous imaging of Ca2+ dynamics in subcellular compartments.

As a universal second messenger, calcium (Ca2+) transmits specific cellular signals via a spatiotemporal signature generated from its extracellular source and internal stores. Our knowledge of the mechanisms underlying the generation of a Ca2+ signature is hampered by limited tools for simultaneously monitoring dynamic Ca2+ levels in multiple subcellular compartments. To overcome the limitation and to further improve spatiotemporal resolutions, we have assembled a molecular toolset (CamelliA lines) in Arabidopsis (Arabidopsis thaliana) that enables simultaneous and high-resolution monitoring of Ca2+ dynamics in multiple subcellular compartments through imaging different single-colored genetically encoded calcium indicators. We uncovered several Ca2+ signatures in three types of Arabidopsis cells in response to internal and external cues, including rapid oscillations of cytosolic Ca2+ and apical plasma membrane Ca2+ influx in fast-growing Arabidopsis pollen tubes, the spatiotemporal relationship of Ca2+ dynamics in four subcellular compartments of root epidermal cells challenged with salt, and a shockwave-like Ca2+ wave propagating in laser-wounded leaf epidermis. These observations serve as a testimony to the wide applicability of the CamelliA lines for elucidating the subcellular sources contributing to the Ca2+ signatures in plants.

Establishment of real-time fluorescence and visual LAMP for rapid detection of Escherichia coli O157:H7 and kits construction.

Escherichia coli O157:H7 is a common pathogenic bacterium in food and water that can pose a threat to human health. The aim of this study was to develop loop-mediated isothermal amplification (LAMP) method for the detection of E. coli O157:H7 in food based on the specific gene Ecs_2840 and to construct rapid detection kits based on the established methods. Specifically, we established two methods of real-time fluorescent LAMP (RT-LAMP) and visual LAMP with calcein as an indicator. In pure bacterial culture, the cell sensitivity and genomic sensitivity of the RT-LAMP kit were 8.8 × 100 CFU ml-1 and 4.61 fg µl-1, respectively. The sensitivity of the visual LAMP kit was 2.35 × 100 CFU ml-1 and 4.61 fg µl-1. Both kits had excellent specificity and anti-interference performance. In addition, milk inoculated with 2.26 × 100 CFU ml-1E. coli O157:H7 could be detected within the reaction time after enrichment for 3 h. The results showed that the LAMP kits were rapid, sensitive, and specific for the detection of E. coli O157:H7 in food and had good application prospects in food safety surveillance.

Learning Interactions in Reaction Diffusion Equations by Neural Networks.

Partial differential equations are common models in biology for predicting and explaining complex behaviors. Nevertheless, deriving the equations and estimating the corresponding parameters remains challenging from data. In particular, the fine description of the interactions between species requires care for taking into account various regimes such as saturation effects. We apply a method based on neural networks to discover the underlying PDE systems, which involve fractional terms and may also contain integration terms based on observed data. Our proposed framework, called Frac-PDE-Net, adapts the PDE-Net 2.0 by adding layers that are designed to learn fractional and integration terms. The key technical challenge of this task is the identifiability issue. More precisely, one needs to identify the main terms and combine similar terms among a huge number of candidates in fractional form generated by the neural network scheme due to the division operation. In order to overcome this barrier, we set up certain assumptions according to realistic biological behavior. Additionally, we use an L2-norm based term selection criterion and the sparse regression to obtain a parsimonious model. It turns out that the method of Frac-PDE-Net is capable of recovering the main terms with accurate coefficients, allowing for effective long term prediction. We demonstrate the interest of the method on a biological PDE model proposed to study the pollen tube growth problem.

[Effects of pH value on stachydrine biosynthesis of hydroponic Leonurus japonicus and its physiological mechanism].

The present study explored the physiological mechanism of the effects of different pH treatments on the growth, physiological characteristics, and stachydrine biosynthesis of Leonurus japonicus to provide references for the cultivation and quality control of L. japonicus. Under hydroponic conditions, different pH treatments(pH 5,6,7,8) were set up. The growth, physiology, and the content of stachydrine and total alkaloids of L. japonicus, as well as the content of key intermediate products in stachydrine biosynthesis pathway(i.e., pyruvic acid, α-ketoglutaric acid, glutamic acid, and ornithine) were monitored to explore the physiological mechanism of the effects of pH on the growth and active components of L. japonicus. The results showed that L. japonicus. could grow normally in the pH 5-8 solution. The pH treatment of neutral acidity was more conducive to the accumulation of photosynthetic pigments and the increase in soluble protein in leaves of L. japonicus. to promote its growth and yield. However, since stachydrine is a nitrogen-containing pyrrolidine alkaloid, its synthesis involves the two key rate-limiting steps of nitrogen addition: reductive ammoniation reaction and Schiff base formation reaction. High pH treatments promote the synthesis and accumulation of substrates and products of the above two reactions, indicating that the alkaline environment can promote the nitrogen addition reaction, thereby promoting the biosynthesis and accumulation of stachydrine.

Machine Learning Aids Classification and Discrimination of Noncanonical DNA Folding Motifs by an Arrayed Host:Guest Sensing System.

An arrayed host:guest fluorescence sensor system can discriminate among and classify multiple different noncanonical DNA structures by exploiting selective molecular recognition. The sensor is highly selective and can discriminate between folds as similar as native G-quadruplexes and those with bulges or vacancies. The host and guest can form heteroternary complexes with DNA strands, with the host acting as mediator between the DNA and dye, modulating the emission. By applying machine learning algorithms to the sensing data, prediction of the folding state of unknown DNA strands is possible with high fidelity.

Strain-GeMS: optimized subspecies identification from microbiome data based on accurate variant modeling.

MOTIVATION: Subspecies identification is one of the most critical issues in microbiome studies, as it is directly related to their functions in response to the environmental stress and their feedbacks. However, identification of subspecies remains a challenge largely due to the small variation between different strains within the same species. Accurate identification of subspecies primarily relies on variant identification and categorization through microbiome data. However, current SNP calling and subspecies identification for microbiome data remain underdeveloped. RESULTS: In this work, we have proposed Strain-GeMS for subspecies identification from microbiome data, based on solid statistical model for SNP calling, as well as optimized procedure for subspecies identification. Results on simulated, ab initio and in vivo datasets have shown that Strain-GeMS could always generate more accurate results compared with other subspecies identification methods. AVAILABILITY AND IMPLEMENTATION: Strain-GeMS is available at: https://github.com/HUST-NingKang-Lab/straingems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Menthol in electronic cigarettes: A contributor to respiratory disease?

Menthol is widely used in tobacco products. This study compared the effects of menthol on human bronchial epithelium using submerged cultures, a VITROCELL® cloud chamber that provides air liquid interface (ALI) exposure without solvents or heating, and a Cultex ALI system that delivers aerosol equivalent to that inhaled during vaping. In submerged culture, menthol significantly increased calcium influx and mitochondrial reactive oxygen species (ROS) via the TRPM8 receptor, responses that were inhibited by a TRPM8 antagonist. VITROCELL® cloud chamber exposure of BEAS-2B monolayers increased mitochondrial protein oxidation, expression of the antioxidant enzyme SOD2, activation of NF-κB, and secretion of inflammatory cytokines (IL-6 and IL-8). Proteomics data collected following ALI exposure of 3D EpiAirway tissue in the Cultex showed upregulation of NRF-2-mediated oxidative stress, oxidative phosphorylation, and IL-8 signaling. Across the three platforms, menthol adversely effected human bronchial epithelium in a manner that could lead to respiratory disease.

Constructing confidence intervals for selected parameters.

In large-scale problems, it is common practice to select important parameters by a procedure such as the Benjamini and Hochberg procedure and construct confidence intervals (CIs) for further investigation while the false coverage-statement rate (FCR) for the CIs is controlled at a desired level. Although the well-known BY CIs control the FCR, they are uniformly inflated. In this paper, we propose two methods to construct shorter selective CIs. The first method produces shorter CIs by allowing a reduced number of selective CIs. The second method produces shorter CIs by allowing a prefixed proportion of CIs containing the values of uninteresting parameters. We theoretically prove that the proposed CIs are uniformly shorter than BY CIs and control the FCR asymptotically for independent data. Numerical results confirm our theoretical results and show that the proposed CIs still work for correlated data. We illustrate the advantage of the proposed procedures by analyzing the microarray data from a HIV study.

Spatiotemporal dynamics of a reaction-diffusion model of pollen tube tip growth.

A reaction-diffusion model is proposed to describe the mechanisms underlying the spatial distributions of ROP1 and calcium on the pollen tube tip. The model assumes that the plasma membrane ROP1 activates itself through positive feedback loop, while the cytosolic calcium ions inhibit ROP1 via a negative feedback loop. Furthermore it is proposed that lateral movement of molecules on the plasma membrane are depicted by diffusion. It is shown that bistable or oscillatory dynamics could exist even in the non-spatial model, and stationary and oscillatory spatiotemporal patterns are found in the full spatial model which resemble the experimental data of pollen tube tip growth.

A Comparison of Base-calling Algorithms for Illumina Sequencing Technology.

Recent advances in next-generation sequencing technology have yielded increasing cost-effectiveness and higher throughput produced per run, in turn, greatly influencing the analysis of DNA sequences. Among the various sequencing technologies, Illumina is by far the most widely used platform. However, the Illumina sequencing platform suffers from several imperfections that can be attributed to the chemical processes inherent to the sequencing-by-synthesis technology. With the enormous amounts of reads produced, statistical methodologies and computationally efficient algorithms are required to improve the accuracy and speed of base-calling. Over the past few years, several papers have proposed methods to model the various imperfections, giving rise to accurate and/or efficient base-calling algorithms. In this article, we provide a comprehensive comparison of the performance of recently developed base-callers and we present a general statistical model that unifies a large majority of these base-callers.

MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data.

MOTIVATION: Single nucleotide variant (SNV) detection procedures are being utilized as never before to analyze the recent abundance of high-throughput DNA sequencing data, both on single and multiple sample datasets. Building on previously published work with the single sample SNV caller genotype model selection (GeMS), a multiple sample version of GeMS (MultiGeMS) is introduced. Unlike other popular multiple sample SNV callers, the MultiGeMS statistical model accounts for enzymatic substitution sequencing errors. It also addresses the multiple testing problem endemic to multiple sample SNV calling and utilizes high performance computing (HPC) techniques. RESULTS: A simulation study demonstrates that MultiGeMS ranks highest in precision among a selection of popular multiple sample SNV callers, while showing exceptional recall in calling common SNVs. Further, both simulation studies and real data analyses indicate that MultiGeMS is robust to low-quality data. We also demonstrate that accounting for enzymatic substitution sequencing errors not only improves SNV call precision at low mapping quality regions, but also improves recall at reference allele-dominated sites with high mapping quality. AVAILABILITY AND IMPLEMENTATION: The MultiGeMS package can be downloaded from https://github.com/cui-lab/multigems CONTACT: xinping.cui@ucr.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Silencing of AtRAP, a target gene of a bacteria-induced small RNA, triggers antibacterial defense responses through activation of LSU2 and down-regulation of GLK1.

Plants fine-tune their sophisticated immunity systems in response to pathogen infections. We previously showed that AtlsiRNA-1, a bacteria-induced plant endogenous small interfering RNA, silences the AtRAP gene, which encodes a putative RNA binding protein. In this study, we demonstrate that AtRAP functions as a negative regulator in plant immunity by characterizing molecular and biological responses of the knockout mutant and overexpression lines of AtRAP upon bacterial infection. AtRAP is localized in chloroplasts and physically interacts with Low Sulfur Upregulated 2 (LSU2), which positively regulates plant defense. Our results suggest that AtRAP negatively regulates defense responses by suppressing LSU2 through physical interaction. We also detected downregulation of the transcription factor GOLDEN2-LIKE 1 (GLK1) in atrap-1 using microarray analysis. The glk1 glk2 double mutant showed enhanced resistance to Pseudomonas syringae pv. tomato, which is consistent with a previous study showing enhanced resistance of a glk1 glk2 double mutant to Hyaloperonospora arabidopsidis. Taken together, our data suggest that silencing of AtRAP by AtlsiRNA-1 upon bacterial infection triggers defense responses through regulation of LSU2 and GLK1.

An improved uniformly more powerful exact Fisher-Hayter pairwise comparisons procedure.

Pairwise comparison is a very common multiple comparison problem. It is known that Fisher's LSD test does not control the familywise error rate (FWER) when there are more than three groups to be compared. Improved testing strategies include the Tukey-Kramer (TK) test that eliminates the F-test step and the two-step Fisher-Hayter (FH) test which requires a significant F-test. We propose a modified FH-test that is uniformly more powerful than the original version and relies on exact size α test under the balanced model. We provide simulations to show that the new procedure is preferred to the FH-test and the TK-test.

A conserved transcriptional regulator is required for RNA-directed DNA methylation and plant development.

RNA-directed DNA methylation (RdDM) is a conserved mechanism for epigenetic silencing of transposons and other repetitive elements. We report that the rdm4 (RNA-directed DNA Methylation4) mutation not only impairs RdDM, but also causes pleiotropic developmental defects in Arabidopsis. Both RNA polymerase II (Pol II)- and Pol V-dependent transcripts are affected in the rdm4 mutant. RDM4 encodes a novel protein that is conserved from yeast to humans and interacts with Pol II and Pol V in plants. Our results suggest that RDM4 functions in epigenetic regulation and plant development by serving as a transcriptional regulator for RNA Pol V and Pol II, respectively.

Mixed directional false discovery rate control in multiple pairwise comparisons using weighted p-values.

In many applications, researchers are interested in making q pairwise comparisons among k test groups on the basis of m outcome variables. Often, m is very large. For example, such situations arise in gene expression microarray studies involving several experimental groups. Researchers are often not only interested in identifying differentially expressed genes between a given pair of experimental groups, but are also interested in making directional inferences such as whether a gene is up- or downregulated in one treatment group relative to another. In such situations, in addition to the usual errors such as false positive (Type I error) and false negative (Type II error), one may commit directional error (Type III error). For example, in a dose response microarray study, a gene may be declared to be upregulated in the high dose group compared to the low dose group when it is not. In this paper, we introduce a mixed directional false discovery rate (mdFDR) controlling procedure using weighted p-values to select positives in different directions. The weights are defined as the inverse of two times the proportion of either positive or negative discoveries. The proposed procedure has been proved mathematically to control the mdFDR at level α and to have a larger power (which is defined as the expected proportion of nontrue null hypotheses) than the GSP10 procedure proposed by Guo et al. (2010). Simulation studies and real data analysis are also conducted to show the outperformance of the proposed procedure than the GSP10 procedure.

Distribution profiling of circulating microRNAs in serum.

Circulating microRNAs (miRNAs) are potential biomarkers useful in cancer diagnosis. They have been found to be bound to various carriers like proteins, lipoprotein particles, and exosomes. It is likely that only miRNAs in particular carriers, but not the overall quantity, are directly related to cancer development. Herein, we developed a method for rapid separation of different miRNA carriers in serum using asymmetrical flow field flow fractionation (AF4). Sera from two healthy individuals (control) or from two cancer patients (case) were fractionated. Six fractions enriching different types of miRNA carriers, such as the lipoprotein particles and exosomes, were collected. The quantities of eight selected miRNAs in each fraction were obtained by RT-qPCR to yield their distribution profiles among the carriers. Larger changes in miRNA quantity between the control and the case were detected in the fractionated results compared to the sum values. Statistical analysis on the distribution profiles also proved that, the quantities of 4 miRNAs within particular fractions showed significant difference between the controls and the cases. On the contrary, if the overall quantity of the miRNA was subject to the same statistical analysis, only 2 miRNAs exhibited significant difference. Moreover, principle component analysis revealed good separation between the controls and the cases with the fractionated miRNA amounts. All in all, we have demonstrated that, our method enables comprehensive screening of the distribution of circulating miRNAs in the carriers. The obtained distribution profile enlarges the miRNA expression difference between healthy individuals and cancer patients, facilitating the discovery of specific miRNA biomarkers for cancer diagnosis.

Introgression of novel traits from a wild wheat relative improves drought adaptation in wheat.

Root architecture traits are an important component for improving water stress adaptation. However, selection for aboveground traits under favorable environments in modern cultivars may have led to an inadvertent loss of genes and novel alleles beneficial for adapting to environments with limited water. In this study, we elucidate the physiological and molecular consequences of introgressing an alien chromosome segment (7DL) from a wild wheat relative species (Agropyron elongatum) into cultivated wheat (Triticum aestivum). The wheat translocation line had improved water stress adaptation and higher root and shoot biomass compared with the control genotypes, which showed significant drops in root and shoot biomass during stress. Enhanced access to water due to higher root biomass enabled the translocation line to maintain more favorable gas-exchange and carbon assimilation levels relative to the wild-type wheat genotypes during water stress. Transcriptome analysis identified candidate genes associated with root development. Two of these candidate genes mapped to the site of translocation on chromosome 7DL based on single-feature polymorphism analysis. A brassinosteroid signaling pathway was predicted to be involved in the novel root responses observed in the A. elongatum translocation line, based on the coexpression-based gene network generated by seeding the network with the candidate genes. We present an effective and highly integrated approach that combines root phenotyping, whole-plant physiology, and functional genomics to discover novel root traits and the underlying genes from a wild related species to improve drought adaptation in cultivated wheat.

SNP calling using genotype model selection on high-throughput sequencing data.

MOTIVATION: A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for. RESULTS: A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers. AVAILABILITY: The GeMS package can be downloaded from https://sites.google.com/a/bioinformatics.ucr.edu/xinping-cui/home/software or http://computationalbioenergy.org/software.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.